import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__a    :	int         =    logging.get_logger(__name__)

__a    :	Optional[Any]         =    OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

__a    :	int         =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
								if class_name in extractors:
												__lowercase					     =				model_type_to_module_name(lowercase				)

												__lowercase					     =				importlib.import_module(F".{module_name}"				,     '''transformers.models'''				)
												try:
																return getattr(lowercase				,     lowercase				)
												except AttributeError:
																continue

				for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
								if getattr(lowercase				,     '''__name__'''				,     lowercase				) == class_name:
												return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
				__lowercase					     =				importlib.import_module('''transformers'''				)
				if hasattr(lowercase				,     lowercase				):
								return getattr(lowercase				,     lowercase				)

				return None





def 							UpperCAmelCase		(       lowercase				,     lowercase = None				,     lowercase = False				,     lowercase = False				,     lowercase = None				,     lowercase = None				,     lowercase = None				,     lowercase = False				,     **lowercase				,     ):







				"""simple docstring"""



				__lowercase					     =				get_file_from_repo(
				    lowercase				,     lowercase				,     cache_dir=lowercase				,     force_download=lowercase				,     resume_download=lowercase				,     proxies=lowercase				,     use_auth_token=lowercase				,     revision=lowercase				,     local_files_only=lowercase				,     )
				if resolved_config_file is None:
								logger.info(
								    '''Could not locate the feature extractor configuration file, will try to use the model config instead.'''				)
								return {}

				with open(lowercase				,     encoding='''utf-8'''				) as reader:
								return json.load(lowercase				)




class   _UpperCamelCase :


			"""simple docstring"""






			def __init__(  self		)	->   Optional[Any]:

							'''simple docstring'''


							raise EnvironmentError(
							    '''AutoFeatureExtractor is designed to be instantiated '''
							    '''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'''		)





			@classmethod
			@replace_list_option_in_docstrings(lowerCAmelCase__		)
			def 			_SCREAMING_SNAKE_CASE     (  cls ,  lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   int:

							'''simple docstring'''


							__lowercase					     =				kwargs.pop('''config''' ,  lowerCAmelCase__		)
							__lowercase					     =				kwargs.pop('''trust_remote_code''' ,  lowerCAmelCase__		)
							__lowercase					     =				True

							__lowercase     ,	__lowercase					     =				FeatureExtractionMixin.get_feature_extractor_dict(lowerCAmelCase__ ,  **lowerCAmelCase__		)
							__lowercase					     =				config_dict.get('''feature_extractor_type''' ,  lowerCAmelCase__		)
							__lowercase					     =				None
							if "AutoFeatureExtractor" in config_dict.get('''auto_map''' ,  {}		):
											__lowercase					     =				config_dict['''auto_map''']['''AutoFeatureExtractor''']

							# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
							if feature_extractor_class is None and feature_extractor_auto_map is None:
											if not isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
															__lowercase					     =				AutoConfig.from_pretrained(lowerCAmelCase__ ,  **lowerCAmelCase__		)
											# It could be in `config.feature_extractor_type``
											__lowercase					     =				getattr(lowerCAmelCase__ ,  '''feature_extractor_type''' ,  lowerCAmelCase__		)
											if hasattr(lowerCAmelCase__ ,  '''auto_map'''		) and "AutoFeatureExtractor" in config.auto_map:
															__lowercase					     =				config.auto_map['''AutoFeatureExtractor''']

							if feature_extractor_class is not None:
											__lowercase					     =				feature_extractor_class_from_name(lowerCAmelCase__		)

							__lowercase					     =				feature_extractor_auto_map is not None
							__lowercase					     =				feature_extractor_class is not None or type(lowerCAmelCase__		) in FEATURE_EXTRACTOR_MAPPING
							__lowercase					     =				resolve_trust_remote_code(
							    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)

							if has_remote_code and trust_remote_code:
											__lowercase					     =				get_class_from_dynamic_module(
											    lowerCAmelCase__ ,  lowerCAmelCase__ ,  **lowerCAmelCase__		)
											__lowercase					     =				kwargs.pop('''code_revision''' ,  lowerCAmelCase__		)
											if os.path.isdir(lowerCAmelCase__		):
															feature_extractor_class.register_for_auto_class()
											return feature_extractor_class.from_dict(lowerCAmelCase__ ,  **lowerCAmelCase__		)
							elif feature_extractor_class is not None:
											return feature_extractor_class.from_dict(lowerCAmelCase__ ,  **lowerCAmelCase__		)
							# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
							elif type(lowerCAmelCase__		) in FEATURE_EXTRACTOR_MAPPING:
											__lowercase					     =				FEATURE_EXTRACTOR_MAPPING[type(lowerCAmelCase__		)]
											return feature_extractor_class.from_dict(lowerCAmelCase__ ,  **lowerCAmelCase__		)

							raise ValueError(
							    F"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
							    F"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
							    F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()		)}"		)





			@staticmethod
			def 			_SCREAMING_SNAKE_CASE     (  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							FEATURE_EXTRACTOR_MAPPING.register(lowerCAmelCase__ ,  lowerCAmelCase__		)
import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							debug_launcher(test_script.main		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							debug_launcher(test_ops.main		)